GenAI + Agentic AI Engineer - S U

Required Skills:

• LangChain / LangGraph

• Agentic AI

• RAG

• Python

• LLM APIs

• Vector Databases

• LangSmith

• Classical ML

• Cloud Platforms

• Prompt Engineering

Nice to Have:

• MCP Tooling

• Multimodal LLMs

• LLM Evaluation

JD #2 — Experience Level: 5–10 Years | GenAI + Agentic + LangChain Ecosystem+ Classical ML 1 Position Overview This role demand strong GenAI experience, emerging mastery in Agentic AI Systems, and a good foundation in classical ML. You will design and build intelligent, tool-using agents, multi-agent systems, RAG pipelines, and LLM-based applications leveraging the LangChain , LangGraph ecosystem, LangSmith for evaluation. Key Responsibilities 1. GenAI / LLM Application Development Build GenAI applications using: LangChain, LangGraph Implement RAG architectures with: Retrieval, reranking, chunking, memory strategies Vector DBs (faiss, aisearch, opensearch, PG vector etc). Design prompt-engineering strategies: Instruction-following ReAct (Reasoning + Acting) Chain-of-thought structuring Self-reflection and planning loops Evaluation Strategy Implement evaluation frameworks for Classical ML and GenAI systems, covering statistical validation, reliability, and robustness. Assess LLM outputs, RAG pipelines, and agent workflows for grounding quality, relevance, and retrieval accuracy (e.g., recall@k, precision@k). Use LangSmith for tracing, automated evaluations, regression testing, and continuous system-level quality monitoring 2. Agentic System Architecture Build agentic workflows: Tool-calling agents Planner–executor systems Multi-agent communication systems Hierarchical agent architectures Deep Agents Integrate memory systems: episodic memory semantic memory vector-based long-term knowledge Implement evaluation frameworks for agentic systems using LangSmith. 3. Model Context Protocol (MCP) & Tooling Implement MCP servers for external tool connectivity. Build tools that allow agents to interact with: APIs Code execution environments Knowledge bases Company applications 4. Classical ML (Foundational DS Skills) Apply ML models to structured/unstructured data. Conduct feature engineering, model selection, hyperparameter tuning. Build interpretable models where required. 5. Engineering & Integration Collaborate with backend engineering teams to seamlessly integrate agentic and GenAI systems into production applications. Implement observability, tracing, and monitoring for GenAI workflows using LangSmith to ensure reliability and system‑level transparency. 6. Cloud ML-Ops & Quality ML Modelling, data drift, concept drift, model quality monitoring. Hands‑on experience across AWS/ Azure/ Databricks, with flexibility to work on any cloud platform. Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown) Required Skills & Experience 5–10 years total experience, with 2–4+ years hands-on GenAI. Hands-on expertise with: LangChain, LangGraph LangSmith (tracing, metrics, evaluations) MCP tooling and agent tool integration ReAct, Tree of Thoughts, multi-agent orchestration RAG patterns and vector databases Strong coding expertise in Python. Classical ML foundations (tree models, regression, etc.). Experience working with LLM APIs and/or open-source LLMs. Experience building and debugging production-quality GenAI pipelines. Aws/azure GIT Ops Prior experience building complex multi-agent systems for real-world applications. Knowledge of multi-modal LLMs (vision, speech, code). Familiarity with structured evaluation of LLM systems (hallucination tests, safety assessments etc ). Experience in enterprise-grade LLM deployments.